POS0892 IDENTIFICATION OF DEFINED MICROANGIOPATHIC CHANGES IN NAILFOLD CAPILLAROSCOPY IMAGES OF PATIENTS WITH SYSTEMIC SCLEROSIS USING A VISION TRANSFORMER MODEL – A MONOCENTRIC IMPLEMENTATION AND VALIDATION COHORT STUDY
Authors: A. Garaiman, F. Nooralahzadeh, C. Mihai, N. Gkikopoulos, N. P. Gonzalez, M. O. Becker, O. Distler, M. Krauthammer, B. Maurer
Journal: Annals of the Rheumatic Diseases (2022)

Background An accurate assessment of nailfold capillaroscopy (NFC) images has great importance in the diagnosis and prognosis of systemic sclerosis (SSc). To overcome some of the inherent problems with NFC image analysis (operator/observer bias, time requirements), there is an interest to automate and standardize NFC image assessment using computer vision algorithms, such as the Vision Transformer (ViT), which are based on recent advances in deep learning. Objectives Our aims were (1) to implement and assess the performance and the reliability of ViT in detecting changes on NFC images, and (2) to compare the performance of ViT to that of practicing rheumatologists. Methods For this study, we used NFC images of patients with SSc enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries. Concretely, we included routine NFC images (all NFC images available – digit II to V of both hands-, irrespective of any image artefacts) of patients aged ≥ 18 years with visits between 2012 and 2021, who fulfilled either the 2013 American College of Rheumatology / European League Against Rheumatism classification criteria for SSc (established disease) or the preliminary criteria for VEDOSS (mild/early disease). ViT was trained to identify the following NFC signs of microangiopathy: enlarged capillaries (apex diameter of the capillary &gt; 20 µm, and &lt; 50µm), giant capillaries (&gt; 50 µm), loss of capillaries (&lt; 7 capillaries/mm), and microhaemorrhages. Absence of any of these signs was considered as being a “normal NFC”, while presence of any of these signs was seen as an abnormal NFC image. Its performance was evaluated in a cross-fold (k = 5) validation setting, and the gold standard was the treating physician. Area under the ROC curve (AUC) was used as indicator of the performance. Considered cut-offs for AUC were 90-100% - excellent, 80-89.9% - good, 70-79.9% - fair, 60-69.9% - poor, 50-59.9% - fail. From the first cross-fold, a randomly sampled reliability set was created which was used to compare the NFC assessment performance of four rheumatologists (three invited and trained annotators and the treating physician) with that of of ViT. Results We analysed 17,126 NFC images of 234 EUSTAR patients (14.6% males, 67.8% limited cutaneous SSc, median age 57 years, median disease duration 9 years) and 55 VEDOSS patients (92.7% females, median age 44 years, median time elapsed since first Raynaud’s phenomenon 5 years). ViT had fair to excellent performance in identifying the different NFC changes across all five folds (of 3443 NFC images each), with an area under the ROC curve ranging from 78.59% to 90.4% (Figure 1a). Figure 1. Performance of the ViT: (a) AUCs values for ViT in diagnosing the respective feature on NFC image. (b) Sensitivity / specificity trade-off across the images in the reliability set. The blue lines show the ROC curves corresponding to the ViT predictions, with ViT’s AUC, “optimal” sensitivity and specificity shown in the box. Also shown are the point performances of each of the four annotators, with performances below the blue line indicating lower performance than ViT. In the reliability set (see Figure 1b), we observed highest performance for diagnosing giant capillaries (AUC =92.89%) followed by identification of enlarged capillaries (AUC = 91.7%). Good AUCs were seen in depicting capillary loss (AUC = 87.3%), microhemorrhages (AUC = 85.9%) and the abnormal/normal NFC classification (AUC = 84.7%). The rheumatologists had generally higher performance in assessing NFC images. However, ViT outperformed two rheumatologists with different experience in classifying capillary loss and enlarged capillaries, respectively. Conclusion ViT is a modern, well performing and readily available AI model to assess signs of microangiopathy on NFC images acquired during routine practice. Disclosure of Interests Alexandru Garaiman: None declared, Farhad Nooralahzadeh: None declared, Carina Mihai Speakers bureau: MEDtalks Switzerland, Mepha, Nikitas Gkikopoulos: None declared, Nicolas Perez Gonzalez: None declared, Mike O. Becker Speakers bureau: Mepha, MSD, Novartis, GSK, Bayer and Vifor, Oliver Distler Speakers bureau: Bayer, Boehringer Ingelheim, Medscape, Novartis, Roche, Pfizer, Roche, Sanofi, Consultant of: Abbvie, Acceleron, Alcimed, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, ChemomAb, Corbus, CSL Behring, Galapagos, Glenmark, GSK, Horizon, Inventiva, iQvia, Kymera, Lupin, Medac, Medscape, Miltenyi Biotec, Mitsubishi Tanabe, MSD, Prometheus Biosences, Roche, Roivant, Topadur and UBC, Lilly, Pfizer, Grant/research support from: Kymera, Mitsubishi Tanabe, Michael Krauthammer Speakers bureau: Oncobit, Britta Maurer Speakers bureau: Boehringer-Ingelheim, Medtalk, Pfizer, Roche, Actelion, Mepha, and MSD, Consultant of: Novartis, Boehringer Ingelheim, Janssen-Cilag, Grant/research support from: AbbVie, Protagen, Novartis Biomedical Research